用聚类节点提升交通预测效率，模型更准更快。
Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting
- 将传感器按空间、功能、行为聚类为高层节点
- 在大规模数据下准确率超现有方法，训练更快
- 适合处理海量交通传感器的实时预测任务
我们提出一种新型图神经网络模型 DeepStateGNN，用于分析交通数据，在交通预测和重构两个关键任务中表现优异。不同于传统 GNN 将每个交通传感器视为独立节点，DeepStateGNN 根据空间接近性（如道路网中相邻传感器）、功能相似性（如同类型高速公路传感器）以及特定条件下行为相似性（如雨天交通行为），将传感器聚类为更高层级的图节点，称为深层状态节点（Deep State Nodes）。该聚类方式支持动态与自适应分组，传感器可隶属多个集群，集群亦可随时间演化。实验表明，DeepStateGNN 具有更优的可扩展性与更快的训练速度，在大规模传感器网络中同时实现更高的预测与重构精度，优于现有方法。
原文摘要 · Abstract (English)
We propose a novel Graph Neural Network (GNN) model, named DeepStateGNN, for analyzing traffic data, demonstrating its efficacy in two critical tasks: forecasting and reconstruction. Unlike typical GNN methods that treat each traffic sensor as an individual graph node, DeepStateGNN clusters sensors into higher-level graph nodes, dubbed Deep State Nodes, based on various similarity criteria, resulting in a fixed number of nodes in a Deep State graph. The term "Deep State" nodes is a play on words, referencing hidden networks of power that, like these nodes, secretly govern traffic independently of visible sensors. These Deep State Nodes are defined by several similarity factors, including spatial proximity (e.g., sensors located nearby in the road network), functional similarity (e.g., sensors on similar types of freeways), and behavioral similarity under specific conditions (e.g., traffic behavior during rain). This clustering approach allows for dynamic and adaptive node grouping, as sensors can belong to multiple clusters and clusters may evolve over time. Our experimental results show that DeepStateGNN offers superior scalability and faster training, while also delivering more accurate results than competitors. It effectively handles large-scale sensor networks, outperforming other methods in both traffic forecasting and reconstruction accuracy.
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